Technicians spend many man hours devising ways to minimize the effects of noise in communication systems and networks and the equipment that compose them. There are many different types of noise that can appear on a communication channel. These noise types include Additive-White-Gaussian Noise (AWGN), narrow-band ingress noise and time-variant burst noise (aka impulse noise). It is understood to those skilled in the art that AWGN and narrow-band ingress noise tend to be relatively constant as a function of time, whereas time-variant burst noise occurs at distinct epochs in time, leaving the channel essentially unaffected in the windows of time between successive bursts.
There are many different techniques that can be used to minimize the effects of noise in a communication system. Some of the well-known techniques in the industry include the use of equalization, the use of Forward-Error Correcting (FEC) codes, the use of lengthened preambles, the use of byte interleaving, the use of CDMA symbol spreading and symbol de-spreading, the use of Trellis-Coded Modulation, the use of notch filters, etc. Each of these techniques can be used effectively for some sub-set of the noise types. For example, notch filters work well for mitigating the effects of ingress noise, but notch filters do not offer much value in mitigating the effects of AWGN or burst noise. As a result, it behooves the technician to have information regarding the types of noise and the parameters of noise that are present on a given channel before selecting which noise mitigation techniques should be applied to that particular channel. This is particularly true for time-variant burst noise.
Time-variant burst noise is characterized by short, quasi-periodic intervals during which the instantaneous noise power exceeds the ambient AWGN background noise. Data transmissions occurring during this noise burst will typically experience a data error rate that is significantly higher than data transmitted when the noise bursts are not present. As a result, standard, well-known methods for calculating data error rates as a function of signal-to-noise ratios (which typically assume the existence of only AWGN sources) do not typically predict actual data error rates when burst noise is present. Thus, alternative methods must be used which can detect and characterize the parameters of the burst noise if one is to correctly predict the data error rates from noise measurements on the channel.
Time-variant burst noise has several interesting parameters associated with it that can help a technician determine the expected data error rates, the types of noise mitigation techniques that should be applied to reduce those data error rates, and the particular settings on those noise mitigation techniques to optimize overall system performance. These burst noise parameters of interest can include the absolute amplitude of the burst noise, the relative amplitude of the burst relative to the background AWGN, the duration of the burst noise, and the period between successive occurrences of bursts. Knowing these parameters can help a field technician determine the minimum amount of noise mitigation to “throw at” the problem (i.e., apply to a noisy desired signal), because the use of excessive noise mitigation often leads to increases in channel overhead and reductions in channel bandwidth that would otherwise be available to the users for data transmission.
Detection and parametric characterization of these noise burst parameters is challenging. Most of the techniques used by field technicians in the past have proposed “looking for,” or detecting, noise in the frequency domain. Unfortunately, time-variant burst noise is difficult to detect in the frequency domain, because it can have spectra that appear very similar to the spectra of AWGN or the spectra of multiple ingress noise sources. Some solutions applied in the field have required the total removal of signal transmissions from the channel. These solutions then use repeated spectrum analyzer measurements (looking for variations of power within the pass-band of successive measurements). These approaches can detect the existence of high-energy noise bursts on a channel, but they do not fare well with lower-energy noise bursts and they do not lend themselves to parametric characterization of the burst noise.
Thus, there is a need in the art for a method and system for detecting the presence of burst noise and for parametrically characterizing the burst noise in a communication system.